TIGER-Lab/OmniEdit-Filtered-1.2M
OmniEdit In this paper, we present OMNI-EDIT, which is an omnipotent editor to handle seven different image editing tasks with any aspect ratio seamlessly. Our contribution is in four folds: (1) OMNI-EDIT is trained by utilizing the supervision from seven different specialist models to ensure task coverage. (2) we utilize importance sampling based on the scores provided by large multimodal models (like GPT-4o) instead of CLIP-score to improve the data quality. 📃Paper | 🌐Website |… See the full description on the dataset page: https://huggingface.co/datasets/TIGER-Lab/OmniEdit-Filtered-1.2M.
| Author / organization | TIGER-Lab |
|---|---|
| License (as declared) | mit |
| Downloads | 60.8K (as reported by Hugging Face) |
| Likes | 136 |
| Created | 11 November 2024 |
| Last updated at source | 6 December 2024 |
More from TIGER-Lab
- TIGER-Lab/MMLU-ProDataset
- TIGER-Lab/AceCode-87KDataset
- TIGER-Lab/MathInstructDataset
Silytics shows public metadata from Hugging Face and links to the original. Information may be incomplete or out of date. Last refreshed 15 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.